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IJMLC 2021 Vol.11(2): 170-175 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.2.1031

Classify Epileptic EEG Signals Using Extreme Support Vector Machine for Ictal and Muscle Artifact Detection

Baiq Siska Febriani Astuti, Santi Wulan Purnami, R. Mohamad Atok, Wardah Rahmatul Islamiyah, Diah Puspito Wulandari, and Anda Iviana Juniani

Abstract—EEG signals aids in diagnosing various wave signals recorded by the activities of the brain. It also produces unavoidable artifacts, in the recording process. The purpose of this study therefore is to detect ictal and artefact signals, with the aim of reducing interpretation errors especially those related to the muscle which are quite difficult to distinguish. The data used are EEG signal recording results obtained from Rumah Sakit Universitas Airlangga. It consisted of two classes, namely ictal and muscle artefact. The signal decomposition method used is a wavelet transform, known as DWT. While the extraction feature utilized, consist of quartile, maximum, minimum, mean and standard deviation. This study also utilized the SVM with linear, polynomial, RBF and ELM (ESVM) kernels. Research results shows that the ESVM classification time is faster than the SVM and other kernels. However, the values of accuracy, sensitivity, specificity and AUC are not better.

Index Terms—ESVM, SVM, wavelet transform, ICTAL, muscle artifact, epilepsy.

Baiq Siska Febriani Astuti, Santi Wulan Purnami, R. Mohamad Atok, and Diah Puspito Wulandari are with the Institut Teknologi Sepuluh Nopember, Indonesia (e-mail: baiqsiskafebriani@gmail.com, santi_wp@its.ac.id, moh_atok@statistics.its.ac.id, diah@te.its.ac.id).
Wardah Rahmatul Islamiyah is with the University of Airlangga, Indonesia (e-mail: wri1905@gmail.com).
Anda Iviana Juniani is with the Shipbuilding Institute of Polytechnic, Indonesia (e-mail: anda.iviana@ppns.ac.id).

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Cite: Baiq Siska Febriani Astuti, Santi Wulan Purnami, R. Mohamad Atok, Wardah Rahmatul Islamiyah, Diah Puspito Wulandari, and Anda Iviana Juniani, "Classify Epileptic EEG Signals Using Extreme Support Vector Machine for Ictal and Muscle Artifact Detection," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 170-175, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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